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  2. Quadratic programming - Wikipedia

    en.wikipedia.org/wiki/Quadratic_programming

    A simple way to see this is to consider the non-convex quadratic constraint x i 2 = x i. This constraint is equivalent to requiring that x i is in {0,1}, that is, x i is a binary integer variable. Therefore, such constraints can be used to model any integer program with binary variables, which is known to be NP-hard.

  3. Constrained least squares - Wikipedia

    en.wikipedia.org/wiki/Constrained_least_squares

    Stochastic (linearly) constrained least squares: the elements of must satisfy = +, where is a vector of random variables such that ⁡ = and ⁡ =. This effectively imposes a prior distribution for β {\displaystyle {\boldsymbol {\beta }}} and is therefore equivalent to Bayesian linear regression .

  4. TK Solver - Wikipedia

    en.wikipedia.org/wiki/TK_Solver

    TK Solver's core technologies are a declarative programming language, algebraic equation solver, [1] an iterative equation solver, and a structured, object-based interface, using a command structure. [ 1 ] [ 7 ] The interface comprises nine classes of objects that can be shared between and merged into other TK files:

  5. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    A pictorial representation of a simple linear program with two variables and six inequalities. The set of feasible solutions is depicted in yellow and forms a polygon, a 2-dimensional polytope. The optimum of the linear cost function is where the red line intersects the polygon.

  6. Lagrange multiplier - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multiplier

    In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equation constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables). [1]

  7. Constrained optimization - Wikipedia

    en.wikipedia.org/wiki/Constrained_optimization

    For very simple problems, say a function of two variables subject to a single equality constraint, it is most practical to apply the method of substitution. [4] The idea is to substitute the constraint into the objective function to create a composite function that incorporates the effect of the constraint.

  8. Branch and cut - Wikipedia

    en.wikipedia.org/wiki/Branch_and_cut

    This description assumes the ILP is a maximization problem.. The method solves the linear program without the integer constraint using the regular simplex algorithm.When an optimal solution is obtained, and this solution has a non-integer value for a variable that is supposed to be integer, a cutting plane algorithm may be used to find further linear constraints which are satisfied by all ...

  9. Big M method - Wikipedia

    en.wikipedia.org/wiki/Big_M_method

    Solve the problem using the usual simplex method. For example, x + y ≤ 100 becomes x + y + s 1 = 100, whilst x + y ≥ 100 becomes x + y − s 1 + a 1 = 100. The artificial variables must be shown to be 0. The function to be maximised is rewritten to include the sum of all the artificial variables.